A long-standing goal of psychopathology research is to develop objective markers of symptomatic states, yet progress has been far slower than expected. Although prior reviews have attributed this state of affairs to diagnostic heterogeneity, symptom comorbidity and phenotypic complexity, little attention has been paid to the implications of intra-individual symptom dynamics and inter-relatedness for biomarker study designs. In this critical review, we consider the impact of short-term symptom fluctuations on widely used study designs that regress the 'average level' of a given symptom against biological data collected at a single time point, and summarize findings from ambulatory assessment studies suggesting that such designs may be sub-optimal to detect symptom-substrate relationships. Although such designs have a crucial role in advancing our understanding of biological substrates related to more stable, longer-term changes (for example, gray matter thinning during a depressive episode), they may be less optimal for the detection of symptoms that exhibit high frequency fluctuations, are susceptible to common reporting biases, or may be heavily influenced by the presence of other symptoms. We propose that a greater emphasis on intra-individual symptom chronometry may be useful for identifying subgroups of patients with common, proximal pathological indicators. Taken together, these three recent developments in the areas of symptom conceptualization and measurement raise important considerations for future studies attempting to identify reliable biomarkers in psychiatry.